Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 10

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1e44fca9ac8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 50

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1e44fdc6d68>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
C:\Users\Ehsan\Anaconda3\envs\face-generation\lib\site-packages\ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    Real_Input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    Z_Data = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    LR = tf.placeholder(tf.float32, shape=(), name='learning_rate')
    return Real_Input, Z_Data, LR

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        #Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        rel1 = tf.maximum(alpha*x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(rel1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        rel2 = tf.maximum(alpha*bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(rel2, 256, 5, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        rel3 = tf.maximum(alpha*bn3, bn3)
        # 7x7x256
        
        #Flatten it
        flat = tf.reshape(rel3, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    keep_prob = 0.5
    with tf.variable_scope('generator', reuse=not is_train):
          
        #Fully connected layer
        x = tf.layers.dense(z, 4*4*1024, activation = None)
        x = tf.reshape(x, (-1, 4, 4, 1024))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        # swish activation
#        x = tf.sigmoid(x)*x
#        x = tf.nn.relu(x)
        # 4x4x1024
        
        x = tf.layers.conv2d_transpose(x, 512, 5, strides=2, padding='same', use_bias= False,
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        # swish activation
#        x = tf.sigmoid(x)*x
#        x = tf.nn.relu(x)
        # 8x8x512
        
        x = tf.layers.conv2d_transpose(x, 256, 5, strides=2, padding='same', use_bias= False,
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        # swish activation
#        x = tf.sigmoid(x)*x
#        x = tf.nn.relu(x)

        # 16x16x256
        
        x = tf.layers.conv2d_transpose(x, 128, 5, strides=2, padding='same', use_bias= False,
                                      kernel_initializer=tf.contrib.layers.xavier_initializer())
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        # swish activation
#        x = tf.sigmoid(x)*x
#        x = tf.nn.relu(x)
        # 32x32x128
        
        x = tf.layers.conv2d_transpose(x, 64, 5, strides=1, padding='same', use_bias= False,
                                      kernel_initializer=tf.contrib.layers.xavier_initializer())
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        # swish activation
#        x = tf.sigmoid(x)*x
#        x = tf.nn.relu(x)
        # 32x32x64
        
        logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides=2, padding='same',
                                           kernel_initializer=tf.contrib.layers.xavier_initializer())
#        logits = tf.contrib.layers.dropout(logits, keep_prob, is_training=is_train)
        logits = tf.image.resize_images(logits, (28, 28))
        output = tf.tanh(logits)
        
    return output

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # label Smoothing factor 
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    # d-loss-real with smoothing factor
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_real)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, alpha, get_batches, data_shape, data_image_mode):
   
    # model
    input_real, input_z, LR = model_inputs(data_shape[1],data_shape[2],data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha)
    
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    losses = []
    steps = 0
    print_every = 20
    
    with tf.Session() as sess:
#        init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
#        sess.run(init_op)
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps +=1
                
                #sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images = batch_images*2
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, LR: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, LR: learning_rate})
    
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                
                    
                if steps % 100 == 0:
                    fig, ax = pyplot.subplots()
                    losses_plot = np.array(losses)
                    pyplot.plot(losses_plot.T[0], label='Discriminator', alpha=0.5)
                    pyplot.plot(losses_plot.T[1], label='Generator', alpha=0.5)
                    pyplot.title("Training Losses")
                    pyplot.legend()
                    pyplot.show()
                    
                    show_generator_output(sess, 25,input_z,data_shape[3],data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 16
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5
alpha = 0.2
# smooth = 0.1
# keep_prob = 0.5
print('batch_size: ', batch_size, '-- z_dim: ', z_dim, '-- LR: ', learning_rate, '-- beta1: ', beta1, '-- alpha: ', alpha)
print('swish activation', '--', 'Generator - Dropout 0.5')

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 4

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
batch_size:  16 -- z_dim:  100 -- LR:  0.0001 -- beta1:  0.5 -- alpha:  0.2
swish activation -- Generator - Dropout 0.5
Epoch 1/4... Discriminator Loss: 0.5673... Generator Loss: 1.9600
Epoch 1/4... Discriminator Loss: 0.5936... Generator Loss: 1.6107
Epoch 1/4... Discriminator Loss: 1.2089... Generator Loss: 0.7008
Epoch 1/4... Discriminator Loss: 0.8565... Generator Loss: 1.4889
Epoch 1/4... Discriminator Loss: 0.9538... Generator Loss: 1.1753
Epoch 1/4... Discriminator Loss: 1.4037... Generator Loss: 1.2131
Epoch 1/4... Discriminator Loss: 0.8277... Generator Loss: 1.2961
Epoch 1/4... Discriminator Loss: 1.2427... Generator Loss: 0.7921
Epoch 1/4... Discriminator Loss: 1.4477... Generator Loss: 0.8102
Epoch 1/4... Discriminator Loss: 1.2678... Generator Loss: 1.0585
Epoch 1/4... Discriminator Loss: 1.0847... Generator Loss: 1.0524
Epoch 1/4... Discriminator Loss: 1.0201... Generator Loss: 1.1703
Epoch 1/4... Discriminator Loss: 1.4230... Generator Loss: 0.6619
Epoch 1/4... Discriminator Loss: 1.5465... Generator Loss: 0.5277
Epoch 1/4... Discriminator Loss: 1.2892... Generator Loss: 1.0055
Epoch 1/4... Discriminator Loss: 0.9080... Generator Loss: 1.3744
Epoch 1/4... Discriminator Loss: 0.9724... Generator Loss: 1.2250
Epoch 1/4... Discriminator Loss: 1.0822... Generator Loss: 1.9937
Epoch 1/4... Discriminator Loss: 1.2054... Generator Loss: 0.8795
Epoch 1/4... Discriminator Loss: 1.2944... Generator Loss: 0.8424
Epoch 1/4... Discriminator Loss: 1.1527... Generator Loss: 0.7274
Epoch 1/4... Discriminator Loss: 0.9580... Generator Loss: 1.1051
Epoch 1/4... Discriminator Loss: 1.2805... Generator Loss: 0.9211
Epoch 1/4... Discriminator Loss: 1.2967... Generator Loss: 0.6460
Epoch 1/4... Discriminator Loss: 1.2379... Generator Loss: 0.9093
Epoch 1/4... Discriminator Loss: 1.3194... Generator Loss: 0.7988
Epoch 1/4... Discriminator Loss: 1.5907... Generator Loss: 0.4454
Epoch 1/4... Discriminator Loss: 0.9289... Generator Loss: 2.2114
Epoch 1/4... Discriminator Loss: 1.4269... Generator Loss: 0.5495
Epoch 1/4... Discriminator Loss: 1.1955... Generator Loss: 0.8217
Epoch 1/4... Discriminator Loss: 1.1694... Generator Loss: 0.9053
Epoch 1/4... Discriminator Loss: 1.3884... Generator Loss: 0.5471
Epoch 1/4... Discriminator Loss: 1.2526... Generator Loss: 0.8467
Epoch 1/4... Discriminator Loss: 1.1079... Generator Loss: 0.9286
Epoch 1/4... Discriminator Loss: 1.5192... Generator Loss: 0.4598
Epoch 1/4... Discriminator Loss: 1.2364... Generator Loss: 0.6761
Epoch 1/4... Discriminator Loss: 1.4129... Generator Loss: 0.9850
Epoch 1/4... Discriminator Loss: 1.2005... Generator Loss: 0.8796
Epoch 1/4... Discriminator Loss: 1.0849... Generator Loss: 0.9384
Epoch 1/4... Discriminator Loss: 1.1845... Generator Loss: 1.1789
Epoch 1/4... Discriminator Loss: 1.3120... Generator Loss: 1.0169
Epoch 1/4... Discriminator Loss: 1.1246... Generator Loss: 1.4346
Epoch 1/4... Discriminator Loss: 1.0722... Generator Loss: 1.1425
Epoch 1/4... Discriminator Loss: 1.4053... Generator Loss: 0.8538
Epoch 1/4... Discriminator Loss: 1.3011... Generator Loss: 0.6168
Epoch 1/4... Discriminator Loss: 1.3234... Generator Loss: 0.7263
Epoch 1/4... Discriminator Loss: 1.2652... Generator Loss: 0.6697
Epoch 1/4... Discriminator Loss: 1.3053... Generator Loss: 0.7704
Epoch 1/4... Discriminator Loss: 1.3861... Generator Loss: 0.9237
Epoch 1/4... Discriminator Loss: 1.2937... Generator Loss: 0.7973
Epoch 1/4... Discriminator Loss: 1.1907... Generator Loss: 0.9373
Epoch 1/4... Discriminator Loss: 1.4281... Generator Loss: 0.7489
Epoch 1/4... Discriminator Loss: 1.4351... Generator Loss: 0.5834
Epoch 1/4... Discriminator Loss: 1.2731... Generator Loss: 1.0353
Epoch 1/4... Discriminator Loss: 1.2918... Generator Loss: 0.8126
Epoch 1/4... Discriminator Loss: 1.2352... Generator Loss: 1.0506
Epoch 1/4... Discriminator Loss: 1.3905... Generator Loss: 0.8657
Epoch 1/4... Discriminator Loss: 1.2138... Generator Loss: 0.6722
Epoch 1/4... Discriminator Loss: 1.0459... Generator Loss: 1.3055
Epoch 1/4... Discriminator Loss: 1.6534... Generator Loss: 1.4219
Epoch 1/4... Discriminator Loss: 1.1832... Generator Loss: 0.7690
Epoch 1/4... Discriminator Loss: 1.1301... Generator Loss: 1.0031
Epoch 1/4... Discriminator Loss: 1.4140... Generator Loss: 0.8598
Epoch 1/4... Discriminator Loss: 1.2078... Generator Loss: 0.9649
Epoch 1/4... Discriminator Loss: 1.4086... Generator Loss: 0.6145
Epoch 1/4... Discriminator Loss: 1.2516... Generator Loss: 0.7628
Epoch 1/4... Discriminator Loss: 1.4601... Generator Loss: 0.6101
Epoch 1/4... Discriminator Loss: 1.2988... Generator Loss: 0.6597
Epoch 1/4... Discriminator Loss: 1.3920... Generator Loss: 0.8587
Epoch 1/4... Discriminator Loss: 1.0945... Generator Loss: 0.9629
Epoch 1/4... Discriminator Loss: 1.5761... Generator Loss: 0.5220
Epoch 1/4... Discriminator Loss: 1.2044... Generator Loss: 0.8874
Epoch 1/4... Discriminator Loss: 1.2253... Generator Loss: 0.8031
Epoch 1/4... Discriminator Loss: 1.4188... Generator Loss: 0.6660
Epoch 1/4... Discriminator Loss: 1.1837... Generator Loss: 0.8391
Epoch 1/4... Discriminator Loss: 1.1558... Generator Loss: 0.9041
Epoch 1/4... Discriminator Loss: 1.1146... Generator Loss: 0.8999
Epoch 1/4... Discriminator Loss: 1.1947... Generator Loss: 0.9772
Epoch 1/4... Discriminator Loss: 1.2716... Generator Loss: 1.1329
Epoch 1/4... Discriminator Loss: 1.4102... Generator Loss: 0.8463
Epoch 1/4... Discriminator Loss: 1.1287... Generator Loss: 0.7254
Epoch 1/4... Discriminator Loss: 1.1375... Generator Loss: 1.1461
Epoch 1/4... Discriminator Loss: 1.4754... Generator Loss: 0.5341
Epoch 1/4... Discriminator Loss: 1.3851... Generator Loss: 0.8199
Epoch 1/4... Discriminator Loss: 1.2153... Generator Loss: 1.0132
Epoch 1/4... Discriminator Loss: 1.1547... Generator Loss: 0.7868
Epoch 1/4... Discriminator Loss: 1.5554... Generator Loss: 0.5425
Epoch 1/4... Discriminator Loss: 1.3914... Generator Loss: 0.7776
Epoch 1/4... Discriminator Loss: 1.4427... Generator Loss: 0.5428
Epoch 1/4... Discriminator Loss: 1.2950... Generator Loss: 0.6310
Epoch 1/4... Discriminator Loss: 1.3466... Generator Loss: 0.8145
Epoch 1/4... Discriminator Loss: 1.1779... Generator Loss: 0.8607
Epoch 1/4... Discriminator Loss: 1.4060... Generator Loss: 0.7186
Epoch 1/4... Discriminator Loss: 1.2515... Generator Loss: 0.9615
Epoch 1/4... Discriminator Loss: 1.1640... Generator Loss: 1.1644
Epoch 1/4... Discriminator Loss: 1.3389... Generator Loss: 1.0803
Epoch 1/4... Discriminator Loss: 1.4554... Generator Loss: 0.5901
Epoch 1/4... Discriminator Loss: 1.0834... Generator Loss: 1.0509
Epoch 1/4... Discriminator Loss: 1.4477... Generator Loss: 0.6715
Epoch 1/4... Discriminator Loss: 1.1164... Generator Loss: 1.0151
Epoch 1/4... Discriminator Loss: 1.1844... Generator Loss: 0.7707
Epoch 1/4... Discriminator Loss: 1.1179... Generator Loss: 1.0138
Epoch 1/4... Discriminator Loss: 1.3569... Generator Loss: 0.7272
Epoch 1/4... Discriminator Loss: 1.4533... Generator Loss: 0.6018
Epoch 1/4... Discriminator Loss: 1.0400... Generator Loss: 0.9509
Epoch 1/4... Discriminator Loss: 1.2357... Generator Loss: 0.7086
Epoch 1/4... Discriminator Loss: 1.4131... Generator Loss: 0.5064
Epoch 1/4... Discriminator Loss: 1.3268... Generator Loss: 0.7172
Epoch 1/4... Discriminator Loss: 1.2613... Generator Loss: 1.1397
Epoch 1/4... Discriminator Loss: 1.3378... Generator Loss: 0.7813
Epoch 1/4... Discriminator Loss: 1.3814... Generator Loss: 0.8227
Epoch 1/4... Discriminator Loss: 1.3946... Generator Loss: 0.6214
Epoch 1/4... Discriminator Loss: 1.4735... Generator Loss: 0.5684
Epoch 1/4... Discriminator Loss: 1.3943... Generator Loss: 0.8391
Epoch 1/4... Discriminator Loss: 1.3103... Generator Loss: 0.7642
Epoch 1/4... Discriminator Loss: 1.3984... Generator Loss: 0.7459
Epoch 1/4... Discriminator Loss: 1.1884... Generator Loss: 1.0923
Epoch 1/4... Discriminator Loss: 1.4182... Generator Loss: 1.2694
Epoch 1/4... Discriminator Loss: 1.4027... Generator Loss: 0.5466
Epoch 1/4... Discriminator Loss: 1.0066... Generator Loss: 1.1077
Epoch 1/4... Discriminator Loss: 1.0695... Generator Loss: 0.9348
Epoch 1/4... Discriminator Loss: 1.4599... Generator Loss: 0.8392
Epoch 1/4... Discriminator Loss: 1.4587... Generator Loss: 0.7217
Epoch 1/4... Discriminator Loss: 1.3499... Generator Loss: 0.6138
Epoch 1/4... Discriminator Loss: 1.2506... Generator Loss: 0.8177
Epoch 1/4... Discriminator Loss: 1.5756... Generator Loss: 0.7786
Epoch 1/4... Discriminator Loss: 1.3557... Generator Loss: 0.6653
Epoch 1/4... Discriminator Loss: 1.1826... Generator Loss: 0.9785
Epoch 1/4... Discriminator Loss: 1.3327... Generator Loss: 0.7682
Epoch 1/4... Discriminator Loss: 1.4143... Generator Loss: 0.5805
Epoch 1/4... Discriminator Loss: 1.1070... Generator Loss: 1.0058
Epoch 1/4... Discriminator Loss: 1.4105... Generator Loss: 0.5601
Epoch 1/4... Discriminator Loss: 1.3215... Generator Loss: 0.7021
Epoch 1/4... Discriminator Loss: 1.3048... Generator Loss: 0.7972
Epoch 1/4... Discriminator Loss: 1.2861... Generator Loss: 0.9437
Epoch 1/4... Discriminator Loss: 1.3706... Generator Loss: 0.6167
Epoch 1/4... Discriminator Loss: 1.3257... Generator Loss: 0.6481
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-12-d8522878ed1a> in <module>()
     17 with tf.Graph().as_default():
     18     train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, mnist_dataset.get_batches,
---> 19           mnist_dataset.shape, mnist_dataset.image_mode)

<ipython-input-11-20874c6209cf> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, alpha, get_batches, data_shape, data_image_mode)
     27                 # Run optimizers
     28                 _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, LR: learning_rate})
---> 29                 _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, LR: learning_rate})
     30 
     31                 if steps % print_every == 0:

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    995     if final_fetches or final_targets:
    996       results = self._do_run(handle, final_targets, final_fetches,
--> 997                              feed_dict_string, options, run_metadata)
    998     else:
    999       results = []

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1130     if handle is None:
   1131       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132                            target_list, options, run_metadata)
   1133     else:
   1134       return self._do_call(_prun_fn, self._session, handle, feed_dict,

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1137   def _do_call(self, fn, *args):
   1138     try:
-> 1139       return fn(*args)
   1140     except errors.OpError as e:
   1141       message = compat.as_text(e.message)

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1119         return tf_session.TF_Run(session, options,
   1120                                  feed_dict, fetch_list, target_list,
-> 1121                                  status, run_metadata)
   1122 
   1123     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.5187... Generator Loss: 2.4927
Epoch 1/1... Discriminator Loss: 0.6054... Generator Loss: 2.9425
Epoch 1/1... Discriminator Loss: 4.2400... Generator Loss: 0.0385
Epoch 1/1... Discriminator Loss: 2.8661... Generator Loss: 0.2427
Epoch 1/1... Discriminator Loss: 1.9105... Generator Loss: 0.5138
Epoch 1/1... Discriminator Loss: 1.8742... Generator Loss: 0.6244
Epoch 1/1... Discriminator Loss: 1.7199... Generator Loss: 0.8379
Epoch 1/1... Discriminator Loss: 1.5613... Generator Loss: 0.6337
Epoch 1/1... Discriminator Loss: 1.4421... Generator Loss: 0.7882
Epoch 1/1... Discriminator Loss: 1.6069... Generator Loss: 0.7711
Epoch 1/1... Discriminator Loss: 1.4584... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.7825... Generator Loss: 0.5601
Epoch 1/1... Discriminator Loss: 2.2642... Generator Loss: 0.3312
Epoch 1/1... Discriminator Loss: 1.2807... Generator Loss: 0.9114
Epoch 1/1... Discriminator Loss: 1.3997... Generator Loss: 0.8108
Epoch 1/1... Discriminator Loss: 1.4979... Generator Loss: 0.6289
Epoch 1/1... Discriminator Loss: 1.5114... Generator Loss: 0.8596
Epoch 1/1... Discriminator Loss: 1.3415... Generator Loss: 0.7112
Epoch 1/1... Discriminator Loss: 1.6688... Generator Loss: 0.5920
Epoch 1/1... Discriminator Loss: 1.5218... Generator Loss: 0.8875
Epoch 1/1... Discriminator Loss: 1.3594... Generator Loss: 0.8780
Epoch 1/1... Discriminator Loss: 1.7529... Generator Loss: 0.7292
Epoch 1/1... Discriminator Loss: 1.7083... Generator Loss: 0.7045
Epoch 1/1... Discriminator Loss: 1.2260... Generator Loss: 0.9190
Epoch 1/1... Discriminator Loss: 1.4766... Generator Loss: 0.7609
Epoch 1/1... Discriminator Loss: 1.4160... Generator Loss: 0.7120
Epoch 1/1... Discriminator Loss: 1.4620... Generator Loss: 0.8216
Epoch 1/1... Discriminator Loss: 1.3211... Generator Loss: 0.7557
Epoch 1/1... Discriminator Loss: 1.4520... Generator Loss: 0.8015
Epoch 1/1... Discriminator Loss: 1.4165... Generator Loss: 0.7844
Epoch 1/1... Discriminator Loss: 1.6540... Generator Loss: 0.6117
Epoch 1/1... Discriminator Loss: 1.5604... Generator Loss: 0.5178
Epoch 1/1... Discriminator Loss: 1.4590... Generator Loss: 0.9295
Epoch 1/1... Discriminator Loss: 1.5426... Generator Loss: 0.9485
Epoch 1/1... Discriminator Loss: 1.3574... Generator Loss: 0.7825
Epoch 1/1... Discriminator Loss: 1.9035... Generator Loss: 0.5445
Epoch 1/1... Discriminator Loss: 1.5338... Generator Loss: 0.5102
Epoch 1/1... Discriminator Loss: 1.3241... Generator Loss: 0.7853
Epoch 1/1... Discriminator Loss: 1.5090... Generator Loss: 0.5978
Epoch 1/1... Discriminator Loss: 1.3949... Generator Loss: 0.7698
Epoch 1/1... Discriminator Loss: 1.4456... Generator Loss: 0.8379
Epoch 1/1... Discriminator Loss: 1.4986... Generator Loss: 0.7204
Epoch 1/1... Discriminator Loss: 1.3231... Generator Loss: 0.7343
Epoch 1/1... Discriminator Loss: 1.3984... Generator Loss: 0.8102
Epoch 1/1... Discriminator Loss: 1.3751... Generator Loss: 0.8356
Epoch 1/1... Discriminator Loss: 1.2720... Generator Loss: 0.7980
Epoch 1/1... Discriminator Loss: 1.5759... Generator Loss: 0.6737
Epoch 1/1... Discriminator Loss: 1.5172... Generator Loss: 0.8752
Epoch 1/1... Discriminator Loss: 1.4609... Generator Loss: 0.6979
Epoch 1/1... Discriminator Loss: 1.2811... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.4886... Generator Loss: 0.8036
Epoch 1/1... Discriminator Loss: 1.5055... Generator Loss: 0.7201
Epoch 1/1... Discriminator Loss: 1.3620... Generator Loss: 0.8180
Epoch 1/1... Discriminator Loss: 1.6081... Generator Loss: 0.5612
Epoch 1/1... Discriminator Loss: 1.5539... Generator Loss: 0.8188
Epoch 1/1... Discriminator Loss: 1.3133... Generator Loss: 0.8145
Epoch 1/1... Discriminator Loss: 1.3988... Generator Loss: 0.8449
Epoch 1/1... Discriminator Loss: 1.3335... Generator Loss: 0.7828
Epoch 1/1... Discriminator Loss: 1.4894... Generator Loss: 0.7452
Epoch 1/1... Discriminator Loss: 1.4163... Generator Loss: 0.7878
Epoch 1/1... Discriminator Loss: 1.3080... Generator Loss: 0.8002
Epoch 1/1... Discriminator Loss: 1.3791... Generator Loss: 0.6981
Epoch 1/1... Discriminator Loss: 1.5305... Generator Loss: 0.6680
Epoch 1/1... Discriminator Loss: 1.4642... Generator Loss: 0.6912
Epoch 1/1... Discriminator Loss: 1.4869... Generator Loss: 0.6631
Epoch 1/1... Discriminator Loss: 1.4098... Generator Loss: 0.7962
Epoch 1/1... Discriminator Loss: 1.4014... Generator Loss: 0.7855
Epoch 1/1... Discriminator Loss: 1.4064... Generator Loss: 0.6935
Epoch 1/1... Discriminator Loss: 1.4424... Generator Loss: 0.6812
Epoch 1/1... Discriminator Loss: 1.4257... Generator Loss: 0.6811
Epoch 1/1... Discriminator Loss: 1.3754... Generator Loss: 0.7986
Epoch 1/1... Discriminator Loss: 1.1876... Generator Loss: 0.9270
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.7824
Epoch 1/1... Discriminator Loss: 1.3516... Generator Loss: 0.8059
Epoch 1/1... Discriminator Loss: 1.2870... Generator Loss: 0.8703
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.8033
Epoch 1/1... Discriminator Loss: 1.3693... Generator Loss: 0.7983
Epoch 1/1... Discriminator Loss: 1.3578... Generator Loss: 0.8213
Epoch 1/1... Discriminator Loss: 1.4302... Generator Loss: 0.8308
Epoch 1/1... Discriminator Loss: 1.4067... Generator Loss: 0.8248
Epoch 1/1... Discriminator Loss: 1.4354... Generator Loss: 0.6605
Epoch 1/1... Discriminator Loss: 1.4363... Generator Loss: 0.7406
Epoch 1/1... Discriminator Loss: 1.3961... Generator Loss: 0.7723
Epoch 1/1... Discriminator Loss: 1.3374... Generator Loss: 0.9278
Epoch 1/1... Discriminator Loss: 1.4757... Generator Loss: 0.6783
Epoch 1/1... Discriminator Loss: 1.2691... Generator Loss: 0.8388
Epoch 1/1... Discriminator Loss: 1.7427... Generator Loss: 0.5268
Epoch 1/1... Discriminator Loss: 1.1970... Generator Loss: 0.8360
Epoch 1/1... Discriminator Loss: 1.3550... Generator Loss: 0.6914
Epoch 1/1... Discriminator Loss: 1.6003... Generator Loss: 0.7258
Epoch 1/1... Discriminator Loss: 1.3942... Generator Loss: 0.7276
Epoch 1/1... Discriminator Loss: 1.4207... Generator Loss: 0.7283
Epoch 1/1... Discriminator Loss: 1.5279... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.5029... Generator Loss: 0.6189
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.9197
Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.9794
Epoch 1/1... Discriminator Loss: 1.5073... Generator Loss: 0.6045
Epoch 1/1... Discriminator Loss: 1.6268... Generator Loss: 0.6290
Epoch 1/1... Discriminator Loss: 1.3560... Generator Loss: 0.9268
Epoch 1/1... Discriminator Loss: 1.4417... Generator Loss: 0.6941
Epoch 1/1... Discriminator Loss: 1.4846... Generator Loss: 0.6641
Epoch 1/1... Discriminator Loss: 1.4720... Generator Loss: 0.6309
Epoch 1/1... Discriminator Loss: 1.6291... Generator Loss: 0.6425
Epoch 1/1... Discriminator Loss: 1.2989... Generator Loss: 0.8857
Epoch 1/1... Discriminator Loss: 1.4404... Generator Loss: 0.8809
Epoch 1/1... Discriminator Loss: 1.2777... Generator Loss: 0.8938
Epoch 1/1... Discriminator Loss: 1.4244... Generator Loss: 0.7631
Epoch 1/1... Discriminator Loss: 1.4376... Generator Loss: 0.7827
Epoch 1/1... Discriminator Loss: 1.3539... Generator Loss: 0.8643
Epoch 1/1... Discriminator Loss: 1.2843... Generator Loss: 0.8410
Epoch 1/1... Discriminator Loss: 1.3947... Generator Loss: 0.7254
Epoch 1/1... Discriminator Loss: 1.4588... Generator Loss: 0.7772
Epoch 1/1... Discriminator Loss: 1.4202... Generator Loss: 0.7308
Epoch 1/1... Discriminator Loss: 1.4713... Generator Loss: 0.6821
Epoch 1/1... Discriminator Loss: 1.5136... Generator Loss: 0.7225
Epoch 1/1... Discriminator Loss: 1.4445... Generator Loss: 0.8996
Epoch 1/1... Discriminator Loss: 1.6837... Generator Loss: 0.6550
Epoch 1/1... Discriminator Loss: 1.4540... Generator Loss: 0.7271
Epoch 1/1... Discriminator Loss: 1.3838... Generator Loss: 0.8194
Epoch 1/1... Discriminator Loss: 1.5313... Generator Loss: 0.7129
Epoch 1/1... Discriminator Loss: 1.3724... Generator Loss: 0.7881
Epoch 1/1... Discriminator Loss: 1.3242... Generator Loss: 0.7708
Epoch 1/1... Discriminator Loss: 1.4147... Generator Loss: 0.8232
Epoch 1/1... Discriminator Loss: 1.3714... Generator Loss: 0.8189
Epoch 1/1... Discriminator Loss: 1.2696... Generator Loss: 0.7583
Epoch 1/1... Discriminator Loss: 1.5467... Generator Loss: 0.6756
Epoch 1/1... Discriminator Loss: 1.4645... Generator Loss: 0.8417
Epoch 1/1... Discriminator Loss: 1.4762... Generator Loss: 0.9049
Epoch 1/1... Discriminator Loss: 1.5296... Generator Loss: 0.7439
Epoch 1/1... Discriminator Loss: 1.3459... Generator Loss: 0.8163
Epoch 1/1... Discriminator Loss: 1.4007... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.5686... Generator Loss: 0.8404
Epoch 1/1... Discriminator Loss: 1.6875... Generator Loss: 0.6999
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.9124
Epoch 1/1... Discriminator Loss: 1.4669... Generator Loss: 0.7684
Epoch 1/1... Discriminator Loss: 1.4817... Generator Loss: 0.7332
Epoch 1/1... Discriminator Loss: 1.4129... Generator Loss: 0.8429
Epoch 1/1... Discriminator Loss: 1.3210... Generator Loss: 0.8360
Epoch 1/1... Discriminator Loss: 1.3544... Generator Loss: 0.8320
Epoch 1/1... Discriminator Loss: 1.4958... Generator Loss: 0.7913
Epoch 1/1... Discriminator Loss: 1.3135... Generator Loss: 0.7569
Epoch 1/1... Discriminator Loss: 1.3784... Generator Loss: 0.7874
Epoch 1/1... Discriminator Loss: 1.4133... Generator Loss: 0.7397
Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 0.8267
Epoch 1/1... Discriminator Loss: 1.4034... Generator Loss: 0.7926
Epoch 1/1... Discriminator Loss: 1.3169... Generator Loss: 0.7768
Epoch 1/1... Discriminator Loss: 1.6183... Generator Loss: 0.6484
Epoch 1/1... Discriminator Loss: 1.5497... Generator Loss: 0.6920
Epoch 1/1... Discriminator Loss: 1.4106... Generator Loss: 0.7506
Epoch 1/1... Discriminator Loss: 1.2224... Generator Loss: 0.9868
Epoch 1/1... Discriminator Loss: 1.4444... Generator Loss: 0.7625
Epoch 1/1... Discriminator Loss: 1.4271... Generator Loss: 0.8368
Epoch 1/1... Discriminator Loss: 1.3116... Generator Loss: 0.8653
Epoch 1/1... Discriminator Loss: 1.4850... Generator Loss: 0.8503
Epoch 1/1... Discriminator Loss: 1.4506... Generator Loss: 0.8854
Epoch 1/1... Discriminator Loss: 1.4180... Generator Loss: 0.7149
Epoch 1/1... Discriminator Loss: 1.4106... Generator Loss: 0.8492
Epoch 1/1... Discriminator Loss: 1.4268... Generator Loss: 0.7147
Epoch 1/1... Discriminator Loss: 1.5653... Generator Loss: 0.7318
Epoch 1/1... Discriminator Loss: 1.4970... Generator Loss: 0.7703
Epoch 1/1... Discriminator Loss: 1.2805... Generator Loss: 0.8514
Epoch 1/1... Discriminator Loss: 1.3316... Generator Loss: 0.7819
Epoch 1/1... Discriminator Loss: 1.2797... Generator Loss: 1.0015
Epoch 1/1... Discriminator Loss: 1.4797... Generator Loss: 0.8377
Epoch 1/1... Discriminator Loss: 1.4042... Generator Loss: 0.9353
Epoch 1/1... Discriminator Loss: 1.5097... Generator Loss: 0.7974
Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 0.7018
Epoch 1/1... Discriminator Loss: 1.3347... Generator Loss: 0.7675
Epoch 1/1... Discriminator Loss: 1.4572... Generator Loss: 0.8269
Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 0.7974
Epoch 1/1... Discriminator Loss: 1.4369... Generator Loss: 0.7732
Epoch 1/1... Discriminator Loss: 1.4017... Generator Loss: 0.7561
Epoch 1/1... Discriminator Loss: 1.4288... Generator Loss: 0.6826
Epoch 1/1... Discriminator Loss: 1.3888... Generator Loss: 0.8139
Epoch 1/1... Discriminator Loss: 1.4328... Generator Loss: 0.8215
Epoch 1/1... Discriminator Loss: 1.4327... Generator Loss: 0.7670
Epoch 1/1... Discriminator Loss: 1.3916... Generator Loss: 0.7733
Epoch 1/1... Discriminator Loss: 1.3875... Generator Loss: 0.8862
Epoch 1/1... Discriminator Loss: 1.4494... Generator Loss: 0.6738
Epoch 1/1... Discriminator Loss: 1.3780... Generator Loss: 0.8123
Epoch 1/1... Discriminator Loss: 1.4114... Generator Loss: 0.8107
Epoch 1/1... Discriminator Loss: 1.3344... Generator Loss: 0.8198
Epoch 1/1... Discriminator Loss: 1.3511... Generator Loss: 0.7783
Epoch 1/1... Discriminator Loss: 1.3190... Generator Loss: 0.7031
Epoch 1/1... Discriminator Loss: 1.2087... Generator Loss: 0.9792
Epoch 1/1... Discriminator Loss: 1.2701... Generator Loss: 0.8389
Epoch 1/1... Discriminator Loss: 1.3376... Generator Loss: 0.7812
Epoch 1/1... Discriminator Loss: 1.4159... Generator Loss: 0.8151
Epoch 1/1... Discriminator Loss: 1.4548... Generator Loss: 0.6956
Epoch 1/1... Discriminator Loss: 1.4387... Generator Loss: 0.7485
Epoch 1/1... Discriminator Loss: 1.4238... Generator Loss: 0.8486
Epoch 1/1... Discriminator Loss: 1.3413... Generator Loss: 0.6695
Epoch 1/1... Discriminator Loss: 1.2924... Generator Loss: 0.8270
Epoch 1/1... Discriminator Loss: 1.3454... Generator Loss: 0.7877
Epoch 1/1... Discriminator Loss: 1.3214... Generator Loss: 0.7636
Epoch 1/1... Discriminator Loss: 1.4334... Generator Loss: 0.6803
Epoch 1/1... Discriminator Loss: 1.2870... Generator Loss: 0.8107
Epoch 1/1... Discriminator Loss: 1.2813... Generator Loss: 0.8673
Epoch 1/1... Discriminator Loss: 1.4602... Generator Loss: 0.7021
Epoch 1/1... Discriminator Loss: 1.3521... Generator Loss: 0.9034
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.7539
Epoch 1/1... Discriminator Loss: 1.4482... Generator Loss: 0.7124
Epoch 1/1... Discriminator Loss: 1.4335... Generator Loss: 0.9504
Epoch 1/1... Discriminator Loss: 1.4520... Generator Loss: 0.7490
Epoch 1/1... Discriminator Loss: 1.5690... Generator Loss: 0.7243
Epoch 1/1... Discriminator Loss: 1.4507... Generator Loss: 0.8188
Epoch 1/1... Discriminator Loss: 1.5528... Generator Loss: 0.7504
Epoch 1/1... Discriminator Loss: 1.5165... Generator Loss: 0.5995
Epoch 1/1... Discriminator Loss: 1.4440... Generator Loss: 0.9409
Epoch 1/1... Discriminator Loss: 1.2799... Generator Loss: 0.8994
Epoch 1/1... Discriminator Loss: 1.4182... Generator Loss: 0.8036
Epoch 1/1... Discriminator Loss: 1.3166... Generator Loss: 0.9113
Epoch 1/1... Discriminator Loss: 1.4117... Generator Loss: 0.8311
Epoch 1/1... Discriminator Loss: 1.4260... Generator Loss: 0.8204
Epoch 1/1... Discriminator Loss: 1.5093... Generator Loss: 0.7120
Epoch 1/1... Discriminator Loss: 1.3896... Generator Loss: 0.7565
Epoch 1/1... Discriminator Loss: 1.3283... Generator Loss: 0.9007
Epoch 1/1... Discriminator Loss: 1.4497... Generator Loss: 0.7899
Epoch 1/1... Discriminator Loss: 1.4525... Generator Loss: 0.7497
Epoch 1/1... Discriminator Loss: 1.4331... Generator Loss: 0.7023
Epoch 1/1... Discriminator Loss: 1.4640... Generator Loss: 0.8169
Epoch 1/1... Discriminator Loss: 1.3939... Generator Loss: 0.8264
Epoch 1/1... Discriminator Loss: 1.3527... Generator Loss: 0.7609
Epoch 1/1... Discriminator Loss: 1.4262... Generator Loss: 0.7889
Epoch 1/1... Discriminator Loss: 1.5068... Generator Loss: 0.6480
Epoch 1/1... Discriminator Loss: 1.4753... Generator Loss: 0.7412
Epoch 1/1... Discriminator Loss: 1.4507... Generator Loss: 0.7910
Epoch 1/1... Discriminator Loss: 1.4388... Generator Loss: 0.6861
Epoch 1/1... Discriminator Loss: 1.3140... Generator Loss: 0.8868
Epoch 1/1... Discriminator Loss: 1.2967... Generator Loss: 0.8467
Epoch 1/1... Discriminator Loss: 1.4225... Generator Loss: 0.7718
Epoch 1/1... Discriminator Loss: 1.3936... Generator Loss: 0.8232
Epoch 1/1... Discriminator Loss: 1.3591... Generator Loss: 0.8116
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.7838
Epoch 1/1... Discriminator Loss: 1.5962... Generator Loss: 0.6686
Epoch 1/1... Discriminator Loss: 1.2740... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 1.4166... Generator Loss: 0.7699
Epoch 1/1... Discriminator Loss: 1.3700... Generator Loss: 0.8208
Epoch 1/1... Discriminator Loss: 1.3646... Generator Loss: 0.7265
Epoch 1/1... Discriminator Loss: 1.3440... Generator Loss: 0.7824
Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.8308
Epoch 1/1... Discriminator Loss: 1.5167... Generator Loss: 0.8366
Epoch 1/1... Discriminator Loss: 1.3785... Generator Loss: 0.8514
Epoch 1/1... Discriminator Loss: 1.3940... Generator Loss: 0.7181
Epoch 1/1... Discriminator Loss: 1.3967... Generator Loss: 0.7169
Epoch 1/1... Discriminator Loss: 1.2270... Generator Loss: 0.8624
Epoch 1/1... Discriminator Loss: 1.5864... Generator Loss: 0.5165
Epoch 1/1... Discriminator Loss: 1.3216... Generator Loss: 0.8549
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.7885
Epoch 1/1... Discriminator Loss: 1.3659... Generator Loss: 0.8738
Epoch 1/1... Discriminator Loss: 1.3564... Generator Loss: 0.7966
Epoch 1/1... Discriminator Loss: 1.3904... Generator Loss: 0.8226
Epoch 1/1... Discriminator Loss: 1.3745... Generator Loss: 0.8485
Epoch 1/1... Discriminator Loss: 1.4170... Generator Loss: 0.7792
Epoch 1/1... Discriminator Loss: 1.3998... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.4749... Generator Loss: 0.8442
Epoch 1/1... Discriminator Loss: 1.3302... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 1.5371... Generator Loss: 0.7745
Epoch 1/1... Discriminator Loss: 1.3200... Generator Loss: 0.8465
Epoch 1/1... Discriminator Loss: 1.5710... Generator Loss: 0.6898
Epoch 1/1... Discriminator Loss: 1.3791... Generator Loss: 0.8180
Epoch 1/1... Discriminator Loss: 1.3770... Generator Loss: 0.7448
Epoch 1/1... Discriminator Loss: 1.3533... Generator Loss: 0.7944
Epoch 1/1... Discriminator Loss: 1.4902... Generator Loss: 0.6523
Epoch 1/1... Discriminator Loss: 1.3979... Generator Loss: 0.7059
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.8624
Epoch 1/1... Discriminator Loss: 1.4479... Generator Loss: 0.7183
Epoch 1/1... Discriminator Loss: 1.6613... Generator Loss: 0.6041
Epoch 1/1... Discriminator Loss: 1.5026... Generator Loss: 0.8335
Epoch 1/1... Discriminator Loss: 1.3865... Generator Loss: 0.7579
Epoch 1/1... Discriminator Loss: 1.4813... Generator Loss: 0.8361
Epoch 1/1... Discriminator Loss: 1.3712... Generator Loss: 0.8209
Epoch 1/1... Discriminator Loss: 1.3845... Generator Loss: 0.7175
Epoch 1/1... Discriminator Loss: 1.4169... Generator Loss: 0.8026
Epoch 1/1... Discriminator Loss: 1.5193... Generator Loss: 0.7258
Epoch 1/1... Discriminator Loss: 1.4964... Generator Loss: 0.7691
Epoch 1/1... Discriminator Loss: 1.5275... Generator Loss: 0.7032
Epoch 1/1... Discriminator Loss: 1.3199... Generator Loss: 0.8038
Epoch 1/1... Discriminator Loss: 1.4554... Generator Loss: 0.7054
Epoch 1/1... Discriminator Loss: 1.3731... Generator Loss: 0.7972
Epoch 1/1... Discriminator Loss: 1.4956... Generator Loss: 0.6826
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 0.9527
Epoch 1/1... Discriminator Loss: 1.3700... Generator Loss: 0.7766
Epoch 1/1... Discriminator Loss: 1.4204... Generator Loss: 0.6724
Epoch 1/1... Discriminator Loss: 1.3759... Generator Loss: 0.8112
Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 0.8050
Epoch 1/1... Discriminator Loss: 1.4513... Generator Loss: 0.7608
Epoch 1/1... Discriminator Loss: 1.2453... Generator Loss: 0.8318
Epoch 1/1... Discriminator Loss: 1.5005... Generator Loss: 0.7052
Epoch 1/1... Discriminator Loss: 1.3252... Generator Loss: 0.8623
Epoch 1/1... Discriminator Loss: 1.4294... Generator Loss: 0.7448
Epoch 1/1... Discriminator Loss: 1.4210... Generator Loss: 0.7147
Epoch 1/1... Discriminator Loss: 1.3782... Generator Loss: 0.7315
Epoch 1/1... Discriminator Loss: 1.5698... Generator Loss: 0.6658
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.7698
Epoch 1/1... Discriminator Loss: 1.3307... Generator Loss: 0.9164
Epoch 1/1... Discriminator Loss: 1.3964... Generator Loss: 0.7859
Epoch 1/1... Discriminator Loss: 1.3519... Generator Loss: 0.7848
Epoch 1/1... Discriminator Loss: 1.3800... Generator Loss: 0.7238
Epoch 1/1... Discriminator Loss: 1.3390... Generator Loss: 0.8236
Epoch 1/1... Discriminator Loss: 1.3136... Generator Loss: 0.8893
Epoch 1/1... Discriminator Loss: 1.3744... Generator Loss: 0.8226
Epoch 1/1... Discriminator Loss: 1.3412... Generator Loss: 0.8011
Epoch 1/1... Discriminator Loss: 1.3658... Generator Loss: 0.7339
Epoch 1/1... Discriminator Loss: 1.3450... Generator Loss: 0.8106
Epoch 1/1... Discriminator Loss: 1.3558... Generator Loss: 0.8221
Epoch 1/1... Discriminator Loss: 1.4187... Generator Loss: 0.8198
Epoch 1/1... Discriminator Loss: 1.4134... Generator Loss: 0.8216
Epoch 1/1... Discriminator Loss: 1.4431... Generator Loss: 0.6999
Epoch 1/1... Discriminator Loss: 1.4430... Generator Loss: 0.7356
Epoch 1/1... Discriminator Loss: 1.3847... Generator Loss: 0.8703
Epoch 1/1... Discriminator Loss: 1.2768... Generator Loss: 0.7846
Epoch 1/1... Discriminator Loss: 1.4772... Generator Loss: 0.7913
Epoch 1/1... Discriminator Loss: 1.4480... Generator Loss: 0.6993
Epoch 1/1... Discriminator Loss: 1.3829... Generator Loss: 0.7924
Epoch 1/1... Discriminator Loss: 1.2467... Generator Loss: 0.8805
Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.8371
Epoch 1/1... Discriminator Loss: 1.4953... Generator Loss: 0.7574
Epoch 1/1... Discriminator Loss: 1.4071... Generator Loss: 0.7449
Epoch 1/1... Discriminator Loss: 1.4071... Generator Loss: 0.6810
Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.7622
Epoch 1/1... Discriminator Loss: 1.3369... Generator Loss: 0.8140
Epoch 1/1... Discriminator Loss: 1.3238... Generator Loss: 0.7233
Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.1633... Generator Loss: 0.8182
Epoch 1/1... Discriminator Loss: 1.3944... Generator Loss: 0.8294
Epoch 1/1... Discriminator Loss: 1.2916... Generator Loss: 0.7309
Epoch 1/1... Discriminator Loss: 1.3882... Generator Loss: 0.7824
Epoch 1/1... Discriminator Loss: 1.4236... Generator Loss: 0.8054
Epoch 1/1... Discriminator Loss: 1.4721... Generator Loss: 0.8083
Epoch 1/1... Discriminator Loss: 1.3577... Generator Loss: 0.9675
Epoch 1/1... Discriminator Loss: 1.3732... Generator Loss: 0.8780
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-13-fae1357bab2b> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-11-20874c6209cf> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, alpha, get_batches, data_shape, data_image_mode)
     27                 # Run optimizers
     28                 _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, LR: learning_rate})
---> 29                 _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, LR: learning_rate})
     30 
     31                 if steps % print_every == 0:

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    995     if final_fetches or final_targets:
    996       results = self._do_run(handle, final_targets, final_fetches,
--> 997                              feed_dict_string, options, run_metadata)
    998     else:
    999       results = []

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1130     if handle is None:
   1131       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132                            target_list, options, run_metadata)
   1133     else:
   1134       return self._do_call(_prun_fn, self._session, handle, feed_dict,

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1137   def _do_call(self, fn, *args):
   1138     try:
-> 1139       return fn(*args)
   1140     except errors.OpError as e:
   1141       message = compat.as_text(e.message)

~\Anaconda3\envs\face-generation\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1119         return tf_session.TF_Run(session, options,
   1120                                  feed_dict, fetch_list, target_list,
-> 1121                                  status, run_metadata)
   1122 
   1123     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.